The use of machine learning for seismic interpretation is a growing area of interest for researchers. Manual interpretation demands time and specialized effort. The use of machine learning model will expedite the process. The Convolutional Neural Networks (CNNs) are a class of deep learning algorithms used for images. In this paper, seismic facies segmentation using encoder-decoder architecture of CNNs is proposed. The proposed method filled the gap using a multimodel approach for seismic interpretation. The novelty of the model is that it is not limited to the current dataset and semantic segmentation models. The encoder-decoder architecture input and output size is the same, and it allows the labelling of each pixel of the image. Four models are trained on the open-sourced F3 block Netherlands dataset. Images of were extracted from the data. Data augmentation is used in two of the models to increase the data size for better model learning. Results of individual models and their ensemble are compared. Ensemble is performed by taking the average of the probabilities of the classes obtained from the trained models. Ensemble gave the superior results. Seven classes are segmented with a global pixel accuracy (GPA) of 98.52%, mean class accuracy (MCA) of 96.88%, and mean intersection over union (MIoU) of 93.92%.

The advancements in Renewable Energy Sources (RES), increasing trends of distributed generation and proliferation of prosumer community requires an affective utilization of energy in Microgrid (MG) paradigm. Peer-to-Peer (P2P) energy trading mechanism provides a platform where prosumers can participate in energy sharing which is beneficial for all users of MG. Meanwhile, due to the high penetration of RES and distributed generations in distribution system used in P2P energy trading concept, the control and operation of existing grid is lacking the required functionalities. In P2P energy trading, power congestion occurs on distribution system which can limit effective utilization of RES. As RES and power demands are increasing, congestion problem also tends to increase. Smart Metering (SM) infrastructure is used to handle Energy Congestion Problem (ECP) by more effective management of prosumer community in P2P energy trading mechanism. SM monitors power transactions, maintains status of the network and obtains power profiles of prosumers. In this paper, Normalized P2P (NP2P) energy trading scheme is proposed with objective to decrease load on main grid and cost minimization for all users. NP2P is centralized contracting scheme treats all users in Microgrid equal for energy flow and cost implementation. Three different cases are considered to validate efficiency of proposed P2P energy trading scheme. In this research work, ECP due to NP2P model is analyzed, modeled as Knapsack Problem (KP) and solved using Greedy and Simulated Annealing (SA) algorithms. In proposed research, Central Energy Management Unit (CEMU) is responsible for implementation of functions for NP2P model and energy congestion control. To improve the reliability of grid, some prosumers with surplus energy should be disconnected. The results of various considered cases for proposed NP2P scheme and algorithms for ECP are compared to verify effectiveness of the solution.

Conventional energy sources (CESs) are currently serving most of the global energy demands, but they will be substantially depleted as moving towards the end of this decade. The generation of electricity from such sources is causing the emission of greenhouse gases that is resulting in deleterious effect on the environment along with changing climatic and energy patterns of the planet. Therefore, the world is heading toward decentralization, and microgrids are playing a key role in this process. The advantages of renewables, which are acknowledged globally as benign, eco-friendly, economical, and inexhaustible resources available worldwide, are to credit for such a massive surge in the utilization of renewable resources in microgrid technology. Despite having an enormous renewable energy potential, Pakistan spends a sizable portion of its budget on energy imports of coal, oil, and liquefied natural gas, however, with good planning, current energy crises might be eliminated or at least mitigated to a greater extent, assuring energy security, economic prosperity, and lower carbon emissions inside the country. This study considers the optimal component planning in a grid-connected microgrid with five objectives to achieve that are to reduce the cost of energy, increase the renewable share, cut greenhouse gas emissions, enhance the reliability of power supply and to make electricity generation sustainable in the long run for the country. Different solar PV capacities are tested against the energy cost, renewable share and emission of greenhouse gases in order to attain the trade-off. The cost of energy is minimized by 92.47%, renewable share rises to 85%, and CO₂ emissions are decreased by 48% for residential application. In the case of commercial application, however, the cost of energy is lowered by 48.52%, the renewable energy share rises to 71.1%, and CO₂ emissions are reduced by 61% through incorporating solar PV into the current power system.

Recent research literature shows promising results by convolutional neural network‐ (CNN‐) based approaches for estimation of traffic matrix of cloud networks using different architectures. Although conventionally, convolutional neural network‐based approaches yield superior estimation; however, these rely on assumptions of availability of a large training dataset which is completely accurate and nonsparse. In real world, both these assumptions are problematic as training data size may be limited, and it is also prone to missing (or incomplete) measurements as well as may have measurement errors. Similarly, the 2‐D training datasets derived from network topology based may be sparse. We investigate these challenges and develop a novel architecture which can cater for these challenges and deliver superior performance. Our approach shows promising results for traffic matrix estimation using convolutional neural network‐based techniques in the presence of limited training data and outlier measurements.

CRYSTALS-Kyber has been standardized by the National Institute of Standards and Technology (NIST) as a quantum-resistant algorithm in the post-quantum cryptography (PQC) competition. The bottleneck in performance of Kyber is the polynomial multiplication based on Number Theoretic transform (NTT). This work presents two parallel architectures adopting Multi-Path Delay Commutator (MDC) approach on target FPGA platform. Resource sharing technique is adopted to perform PWM operations using MDC NTT/INTT architecture. Moreover, we propose various optimizations at architectural level to minimize resource consumption such as FIFO-based memory units for buffering of input output coefficients, LUT-based modular multiplier and distributed-ROM memories for twiddle factor storage. The presented architectures are implemented on Xilinx Artix-7 XC7A100T-3 device using Vivado Design Suite 2022.2 and coded using Verilog HDL. Our BRAM and DSP-free designs achieve 68% improved area-time product with a comparable ATP for PWM operations. Additionally, the two-parallel MDC architecture outperforms state-of-the-art architectures, using 29% fewer resources.